Designing a schema means defining how entities and relationships will be represented, typed, and queried. A good schema balances flexibility with consistency. Developers start by listing key entity types (e.g., people, products, events) and the relationships that connect them. Each type includes attributes—names, categories, timestamps, or metrics—modeled as node or edge properties.
Graph schema design also considers query intent. If the system must support semantic retrieval, attributes likely to be searched—such as descriptions or abstracts—should be embedded and indexed. Logical relationships, like parent-child hierarchies or temporal links, are defined for efficient traversal. Schema evolution is handled through versioning to accommodate new entity classes without breaking existing queries.
When using Zilliz, schema design extends to embedding storage. Each entity type can have its own collection in Zilliz, optimized for similarity search. For example, “product” and “article” embeddings might use different vector dimensions or distance metrics. This modularity keeps retrieval fast and targeted, ensuring that the graph remains performant even as it scales.